15 August 2023
Electronics | Highly Cited Papers in 2021 in the Section “Bioelectronics”


The primary focus of the “Bioelectronics” Section are the topics that seek to exploit electronics knowledge and execution in the field of biology and medicine for health wellness with research efforts that cross disciplines such as chemistry, life science, physics, electrical engineering, and materials science. Bioelectronic research works in a wide context, encompassing, for example, biosensors, bionics and biomaterials, DNA chips, lab-on-a-chip, innovative devices, or advanced signal processes for the prevention, diagnosis, and treatment of physical and mental diseases, robotic devices for patient rehabilitation, bioelectromagnetics, artificial intelligence for improving health, conductive polymers, organic semiconductors, carbon nanotubes, graphene, wearable electronics, and implantable electronics, just to cite a few.

As they are of an open access format, you have free and unlimited access to the full text of all the articles published in our journal. We welcome you to read our most highly cited papers published in 2021 below:

1. “CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope”
by Dulari Bhatt, Chirag Patel, Hardik Talsania, Jigar Patel, Rasmika Vaghela, Sharnil Pandya, Kirit Modi and Hemant Ghayvat
Electronics 2021, 10(20), 2470; https://doi.org/10.3390/electronics10202470
Available online: https://www.mdpi.com/2079-9292/10/20/2470

2. “Automated Workers’ Ergonomic Risk Assessment in Manual Material Handling Using sEMG Wearable Sensors and Machine Learning”
by Srimantha E. Mudiyanselage, Phuong Hoang Dat Nguyen, Mohammad Sadra Rajabi and Reza Akhavian
Electronics 2021, 10(20), 2558; https://doi.org/10.3390/electronics10202558
Available online: https://www.mdpi.com/2079-9292/10/20/2558

3. “GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals”
by Hui Wen Loh, Chui Ping Ooi, Elizabeth Palmer, Prabal Datta Barua, Sengul Dogan, Turker Tuncer, Mehmet Baygin and U. Rajendra Acharya
Electronics 2021, 10(14), 1740; https://doi.org/10.3390/electronics10141740
Available online: https://www.mdpi.com/2079-9292/10/14/1740

4. “Integration and Applications of Fog Computing and Cloud Computing Based on the Internet of Things for Provision of Healthcare Services at Home”
by Muhammad Ijaz, Gang Li, Ling Lin, Omar Cheikhrouhou, Habib Hamam and Alam Noor
Electronics 2021, 10(9), 1077; https://doi.org/10.3390/electronics10091077
Available online: https://www.mdpi.com/2079-9292/10/9/1077

5. “State-of-the-Art Optical Devices for Biomedical Sensing Applications—A Review”
by N. L. Kazanskiy, S. N. Khonina, M. A. Butt, A. Kaźmierczak and R. Piramidowicz
Electronics 2021, 10(8), 973; https://doi.org/10.3390/electronics10080973
Available online: https://www.mdpi.com/2079-9292/10/8/973

6. “An Overview of Wearable Piezoresistive and Inertial Sensors for Respiration Rate Monitoring”
by Roberto De Fazio, Marco Stabile, Massimo De Vittorio, Ramiro Velázquez and Paolo Visconti
Electronics 2021, 10(17), 2178; https://doi.org/10.3390/electronics10172178
Available online: https://www.mdpi.com/2079-9292/10/17/2178

7. “Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling”
by Muhammad Wasimuddin, Khaled Elleithy, Abdelshakour Abuzneid, Miad Faezipour and Omar Abuzaghleh
Electronics 2021, 10(2), 170; https://doi.org/10.3390/electronics10020170
Available online: https://www.mdpi.com/2079-9292/10/2/170

8. “Smart E-Textile Systems: A Review for Healthcare Applications”
by Shahood uz Zaman, Xuyuan Tao, Cedric Cochrane and Vladan Koncar
Electronics 2022, 11(1), 99; https://doi.org/10.3390/electronics11010099
Available online: https://www.mdpi.com/2079-9292/11/1/99

9. “Innovative IoT Solutions and Wearable Sensing Systems for Monitoring Human Biophysical Parameters: A Review”
by Esteban Piacentino, Massimo De Vittorio and Paolo Visconti
Electronics 2021, 10(4), 389; https://doi.org/10.3390/electronics10040389
Available online: https://www.mdpi.com/2079-9292/10/4/389

10. “Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography”
by Imayanmosha Wahlang, Arnab Kumar Maji, Goutam Saha, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz and Elzbieta Jasinska
Electronics 2021, 10(4), 495; https://doi.org/10.3390/electronics10040495
Available online: https://www.mdpi.com/2079-9292/10/4/495

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